Open AccessGenerative Adversarial Domain Adaptation for Nucleus Quantification in Images of Tissue Immunohistochemically Stained for Ki-67
Author(s) -
Xuhong Zhang,
Toby C. Cornish,
Lin Yang,
Tellen D Bennett,
Debashis Ghosh,
Fuyong Xing
Publication year - 2020
Publication title -
jco clinical cancer informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.188
H-Index - 12
ISSN - 2473-4276
DOI - 10.1200/cci.19.00108
Subject(s) - artificial intelligence , computer science , deep learning , pattern recognition (psychology) , convolutional neural network , nucleus , domain adaptation , data set , set (abstract data type) , biology , classifier (uml) , programming language , microbiology and biotechnology
We focus on the problem of scarcity of annotated training data for nucleus recognition in Ki-67 immunohistochemistry (IHC)-stained pancreatic neuroendocrine tumor (NET) images. We hypothesize that deep learning-based domain adaptation is helpful for nucleus recognition when image annotations are unavailable in target data sets.